A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon

Boroojeni, Kianoosh G.; Amini, M. Hadi; Bahrami, Shahab; Iyengar, S. S.; Sarwat, Arif I.; Karabaşoğlu, Orkun

doi:10.1016/j.epsr.2016.08.031

Cited by 204 publications

(81 citation statements)

References 38 publications

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“…And the other is the novel prediction method of artificial intelligence class, such as expert systems and artificial neural networks. Because there are many factors affecting the shortterm power load and different prediction methods have different applications, none of these methods is applicable to all power systems, which need to choose different prediction models according to different power load conditions [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Fan

Duan

et al. 2018

Mathematical Problems in Engineering

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In order to improve the prediction accuracy, this paper proposes a short-term power load forecasting method based on the improved exponential smoothing grey model. It firstly determines the main factor affecting the power load using the grey correlation analysis. It then conducts power load forecasting using the improved multivariable grey model. The improved prediction model firstly carries out the smoothing processing of the original power load data using the first exponential smoothing method. Secondly, the grey prediction model with an optimized background value is established using the smoothed sequence which agrees with the exponential trend. Finally, the inverse exponential smoothing method is employed to restore the predicted value. The first exponential smoothing model uses the 0.618 method to search for the optimal smooth coefficient. The prediction model can take the effects of the influencing factors on the power load into consideration. The simulated results show that the proposed prediction algorithm has a satisfactory prediction effect and meets the requirements of short-term power load forecasting. This research not only further improves the accuracy and reliability of short-term power load forecasting but also extends the application scope of the grey prediction model and shortens the search interval.

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Section: Introductionmentioning

confidence: 99%

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Fan

Duan

et al. 2018

Mathematical Problems in Engineering

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“…The two-tiers are a maximum likelihood estimator for longer durations and an ARIMA model for short term forecasting [20] . They have also used a multiseasonal ARIMA model to forecast the PJM interconnection [21] .…”

Section: Section II Previous Workmentioning

confidence: 99%

Data Improving in Time Series Using ARX and ANN Models

Akouemo

Povinelli

2017

IEEE Trans. Power Syst.

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Abstract:Anomalous data can negatively impact energy forecasting by causing model parameters to be incorrectly estimated. This paper presents two approaches for the detection and imputation of anomalies in time series data. Autoregressive with exogenous inputs (ARX) and artificial neural network (ANN) models are used to extract the characteristics of time series. Anomalies are detected by performing hypothesis testing on the extrema of the residuals, and the anomalous data points are imputed using the ARX and ANN models. Because the anomalies affect the model coefficients, the data cleaning process is performed iteratively. The models are re-learned on "cleaner" data after an anomaly is imputed. The anomalous data are reimputed to each iteration using the updated ARX and ANN models. The ARX and ANN data cleaning models are evaluated on natural gas time series data. This paper demonstrates that the proposed approaches are able to identify and impute anomalous data points. Forecasting models learned on the unclean data and the cleaned data are tested on an uncleaned out-of-sample dataset. The forecasting model learned on the cleaned data outperforms the model learned on the unclean data with 1.67% improvement in the mean absolute percentage errors and a 32.8% improvement in the root mean squared error. Existing challenges include correctly identifying specific types of anomalies such as negative flows.

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“…The concept of multiple time grids has been explored in the field of process systems engineering (e.g. [16,17,18] and electric power system (e.g [19,20,21]). Nevertheless, its implementation on energy systems with different types of storage is less well studied, particularly for systems with seasonal storage.…”

Section: Introductionmentioning

confidence: 99%

Multiple time grids in operational optimisation of energy systems with short- and long-term thermal energy storage

Renaldi

Friedrich

2017

Energy

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As a vital part of future low carbon energy systems, storage technologies need to be included in the overall optimisation of energy systems. However, this comes with a price of increasing complexity and computational cost. The increase in complexity can be limited by using simplified time series formulations in the optimisation process, e.g. typical days or multiple time grids. This in turn will affect the computational cost and quality of the optimisation results. The trade-off between these two aspects has to be quantified in order to appropriately use the simplification method. This paper investigates the implementation of the multiple time grids approach in the optimisation of a solar district heating system with short-and long-term thermal energy storage. The multiple time grids can improve the optimisation computational time by over an order of magnitude. Nevertheless, this is not a general rule since it is shown that there is a possibility for the computational time to increase with time step size. Furthermore, the benefits of multiple time grids become more evident in optimisation with a longer time horizon, reaching almost two order of magnitude improvement in computational time for the case with 6 years time horizon and 5% MIP gap.

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A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon

Cited by 204 publications

References 38 publications

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model

Data Improving in Time Series Using ARX and ANN Models

Multiple time grids in operational optimisation of energy systems with short- and long-term thermal energy storage

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